Frequency domain interest point descriptor

ABSTRACT

Systems and methods for image analysis and recognition are disclosed, in particular the methods for interest point description. An interest point and its surrounding area is broken into subareas, a frequency domain description of each area is created by applying discrete Fourier transform (DFT). Frequency domain features are than coded bitwise by comparing them to predefined thresholds. Subsequently, the present invention provides alternative or improved methods and data structures for interest point description that may reduce memory consumption and allow fast bitwise matching.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional patent application No. 61/712,304, filed 2012 Oct. 11 by the present inventor.

FIELD OF THE INVENTION

The present invention relates to methods and apparatus for operating on images, in particular methods and apparatus for description and matching of interest points either in the same or in a different image. The present invention also relates to measuring the similarity score for pairs of image features.

BACKGROUND OF THE INVENTION

Modern computer vision applications are widely used for a range of practical purposes including image registration, object recognition and 3D reconstruction to name a few. Derivation of mathematically processable representation of an image is a prerequisite of any such application or algorithm. In most existing models such representation is created using the following stages:

-   -   1. Interest point detection, i.e. detection of image features         robust to noise, detection errors and geometric and photometric         deformations.     -   2. Feature description, i.e. creation of distinctive         mathematical description providing the means to numerically         measure their similarity.     -   3. Matching of pairs of feature descriptors based on their         similarity scores, e.g. Euclidean distances between descriptor         vectors.

See FIG. 3.

Historically, most interest point detectors are based on first and second spatial derivatives. Most notable representatives are Harris corner detector [Harris C., 1988] using second-moment matrix and its scale invariant modifications, such Laplacian of Gaussian, SIFT's difference of Gaussians [Lowe, 2004] and Fast-Hessian detector used in SURF [Herbert Bay, 2008]. Among these, SURF's Fast-Hessian detector shows the best performance while producing output robust to distortions and lighting changes. Out of non-Harris-based detectors, FAST [Rosten and Drummond, 2006] is worth mentioning. FAST outperforms SURF, but is not scale-invariant. As a result of interest point detection, pair of coordinates and scale (x,y,σ) are produced and passed as an input to feature descriptor.

Most notable feature descriptor implementations are SIFT and SURF [Lowe, 2000, Funayama, 2012], that are considered industry standard for modern applications. However, the size of these descriptors varies from 144 to 512 bytes which imposes serious memory consumption limitations on large scale applications. Theoretically, a 144-byte (1152-bit) descriptor may distinguish between 2¹¹⁵² different objects. This is far more than required in any practical application. Furthermore, SIFT and SURF descriptors are based on vectors of floating point numbers, therefore distance computation requires a lot of time-consuming floating point operations.

Accordingly, what is desired, and not heretofore been developed, is a feature descriptor that uses only bitwise and integer operations for distance computation and has a significantly smaller memory footprint. Developing such a descriptor would considerably speed up the matching step for large image databases.

BRIEF SUMMARY OF THE INVENTION

It is an object of the present invention to provide a distinct and compact interest point descriptor. The descriptor is created by dividing an interest point and its surroundings into subareas, the actual method of division being a trade-off between performance and distinctiveness. Afterwards, each of the subareas is described independently.

First, a frequency domain representation is created by applying DFT. Then, after optional normalization step providing contrast invariance, frequencies to encode are selected and the value at each of them is compared to a predefined threshold assigned to that frequency. Subsequently, a bitwise description of the frequency domain is created as a result of this comparison. See FIG. 10.

Obtained subarea descriptor bit strings are appended into a single descriptor of the whole area. The distance between the descriptors is defined as the Hamming distance in the matching stage. This allows to perform matching substantially faster than with previous descriptors.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 2 is an example of pairs of features matched between two images.

FIG. 3 illustrates general approach to image recognition. The stage where the present invention is used is highlighted.

FIG. 4 describes the interaction between interest point detector and descriptor.

FIG. 5 describes the interaction between interest point descriptor and feature matcher.

FIG. 6 shows optional data provided to feature matcher by interest point detector.

FIG. 1 marks variously sized areas around an interest point.

FIG. 7 illustrates the division of the described area into smaller subareas.

FIG. 8 provides some examples of alternative subarea division.

FIG. 9 shows typical spatial domain representations of frequency domain features for 8×8 F_(ij) matrix.

FIG. 10 illustrates the description creation process for a single rectangular area by example.

FIG. 11 provides an example of Hamming distance computation.

DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

The present invention provides methods and apparatus for creation of distinctive and compact data structures (descriptors), that may be used as compact representative signatures of a digital image or its specific areas. The descriptors created by the embodiments of the present invention enable to quantitively measure the similarity of a pair of interest points either in the same or in a different image.

The methods of the present invention are used as a second step in a sequence of three independent and standalone steps: interest point detection, feature description and feature matching (See FIG. 3). Therefore, the present invention requires an interest point detector to provide coordinates (x,y) and scale σ as an input (FIG. 4). The present invention is not limited to use with conjunction with any specific interest point detector, but to provide useful results during the feature matching stage (FIG. 5) it requires the detector to repeatably produce interest points that are robust to image distortions and lighting or color changes. Optionally, interest point detector may output the sign of Laplacian in the center of the detected interest point. The sign of Laplacian may be used during the matching stage to eliminate false negatives and improve matching performance, but it doesn't have any impact on the operation of the feature description stage (FIG. 5).

The coordinates (x,y) provided by an interest point detector are used to determine the center of the image area to describe. The area used to compute a descriptor is a square with its linear size proportional to σ, i.e. if we define a as the side length of the described area, a=kσ. Coefficient k may vary in reasonable measures around about 10. Extremely large values of k extend the described area to include too much of surroundings thus reducing emphasis on the interest point itself, whereas the values of k that are too small produce descriptors that are not distinctive because they only describe the local luminosity extremum in the center of an interest point. See FIG. 1

The described area is afterwards divided into smaller subareas, each to form an independent chunk of the descriptor. Such division may be performed in various ways, but for practical purposes the following division method was observed to show the best results:

-   -   1. The first subarea is the area itself.     -   2. 9 more subareas are formed by dividing the area into 9         squares equal in size having the edge length equal to a/3.         See FIG. 7 for clarification and FIG. 8 for examples of         alternative division methods.

Each of the described subareas is divided into a uniform orthogonal grid, for illustrative embodiment assume the grid having 8×8 cells, but it can be any size depending on performance, compactness and distinctiveness requirements. Afterwards, average luminosity is calculated for each cell of the grid, thus forming a 8×8 integer matrix L_(xy). Optionally we can increase performance with the use of integral images [Crow, 1984] making computation of the average luminosity a constant complexity operation with regards to cell size. Then the luminosity matrix is transformed to frequency domain by applying DFT, specifically DCT [Ahmed N. and R., 1974] was used for illustrative embodiment. As soon as DCT is quite an expensive operation in terms of performance, it's reasonable to use integer DCT instead of usual float DCT. As a result of this operation we derive a new frequency domain 8×8 matrix F_(ij).

$F_{ij} = {\frac{1}{4}C_{i}C_{j}{\sum\limits_{x,{y = 0}}^{7}\; {L_{xy}\cos \frac{\left( {{2x} + 1} \right)\; \pi}{16}\cos \frac{\left( {{2y} + 1} \right)j\; \pi}{16}}}}$ where $C_{i},{C_{j} = \left\{ \begin{matrix} \frac{1}{\sqrt{2}} & {{{for}\mspace{14mu} i},{j = 0}} \\ 1 & {otherwise} \end{matrix} \right.}$

By describing an image area in the frequency domain we get 64 numbers, each describing an independent visual peculiarity of an image, in contrary to the spacial domain, where none of the pixels anyhow describes an image or its area as a whole. For example, the F₀₀ coefficient corresponds to the average luminosity in the area, F₁₀ characterizes the gradient along the X axis, F₀₁ is likewise proportional to the gradient along the Y axis and F₂₂ corresponds to a light or a dark blob in the center of the area depending on its sign. FIG. 9 illustrates how different frequency domain features (represented by F_(ij) with i and j ranging from 0 to 7) look in spatial domain, i.e. how they are represented in the image being described.

As soon as the F₀₀ coefficient corresponds to average luminosity, it should be discarded to make the descriptor robust to lighting changes. Afterwards, we also make the descriptor invariant to contrast changes by normalizing the rest of F_(ij):

$f_{ij} = \frac{F_{ij}}{\sum\limits_{{i + j} \neq 0}^{\;}\; {F_{ij}}}$

To improve performance, normalization may also be limited to only integer operations:

$f_{ij} = \frac{{CF}_{ij}}{\sum\limits_{{i + j} \neq 0}^{\;}\; {F_{ij}}}$

where C is an arbitrary sufficiently large constant, e.g. 2¹⁶. After normalization, each of the f_(ij) coefficients shows the significance of the feature corresponding to that coefficient in comparison to all the other features. Thus, visually predominant features will have large absolute values of their corresponding f_(ij) coefficients.

It is useful to discard the high frequency part of f_(ij) because high frequency features are rarely important and often correspond to image noise. This also helps keeping the descriptor as compact as possible. Thanks to visual independence of the features described by the f_(ij) coefficients, each of the remaining coefficients may be stored in just 1 bit with 1-bit denoting a large absolute value of that coefficient and 0-bit denoting f_(ij) being close to zero. In order to perform such classification, each of |f_(ij)| should be compared to predefined threshold T_(ij). Note that the threshold may not be the same for different frequencies, i.e. for higher frequencies the values of f_(ij) are typically smaller than for low frequencies, so the thresholds T_(ij) should be smaller as well.

The exact threshold values T_(ij) vary depending on the type of images we operate with and should be fine tuned for concrete implementations. One of good ways to define T_(ij) is using the statistical approach. First, a training set of images is selected depending on the application domain. Then f_(ij) is computed for each interest point in each of the images from the training set. Afterwards, for each pair of i and j a median m_(ij) of the list of computed f_(ij) is calculated. The value of m_(ij) may be either directly used as a threshold T_(ij) or somehow transformed. However, any deviations from the median values in T_(ij) would lead to reduction of descriptor entropy and therefore distinctiveness.

By the thresholding operation described above the memory footprint of the descriptor chunk is reduced to N bits, where N is the number of non-discarded f_(ij). To improve the descriptor distinctiveness for the price of its size, significant negative and significant positive f_(ij) coefficients may be stored separately, thus increasing the descriptor chunk to 2N bits. For example, for a 10-chunk descriptor (i.e. the one describing the whole feature area and its 9 subareas) this leads to a 10N-bit descriptor (20N-bit for signed variant). For practical purposes it is sufficient to use N=8, so that the descriptor takes only 10 bytes (20 bytes for signed alternative).

Example of the descriptor chunk creation process is shown in FIG. 10.

In contrary to SIFT or SURF descriptors where the distance is defined as an Euclidean distance between the descriptor vectors, in frequency domain descriptor the distance is defined as a Hamming distance between the two bit sequences. Thus, the distance between descriptors a and b may be calculated as the Hamming weight of âb, where ̂ denotes bitwise XOR (see FIG. 11). This operation may be efficiently performed with a number of algorithms with O(log n) worst-case complexity where n is the descriptor bit string length. Moreover, most modern processors include a separate instruction for Hamming weight computation, e.g. POPCNT instruction in the SSE4.2 instruction set.

Embodiment of the present invention may be created using software, hardware or the combination thereof. Current description of the illustrative embodiment is focused on the software implementation of the present invention. However, any of the parts and methods of the present invention or the whole of it may be implemented as hardware circuit.

CONCLUSION, RAMIFICATIONS, AND SCOPE

The reader will see that, according to one embodiment of the invention, we have provided a method for computing distinctive descriptors of interest points and their surroundings that are both compact and efficient during the matching stage.

While the above description contains many specificities, these should not be seen as limitations on the scope of any embodiment, but as exemplifications of the presently preferred embodiments thereof. Many other ramifications and variations are possible within the teachings of the various embodiments. For example, the embodiment described above operates on 2D images but the present invention may be extended to 3D solid images, or spatio-temporal domains, such as video. Also the present invention was mainly described with reference to gray scale images but the present invention is not limited thereto. The present invention may also be applied to color images, however any such application implies transformation of color image or its specific areas to gray scale in one way or another.

Thus the scope of the invention should be determined by the appended claims and their legal equivalents, and not by the examples given.

REFERENCES

-   [Ahmed N. and R., 1974] “Discrete cosine transform” (Ahmed N.,     Natarajan T. and Rao K. R.). -   [Bissacco, 2011] U.S. Pat. No. 8,086,616 (Bissacco, et al.). -   [Crow, 1984] “Summed-area tables for texture mapping” (Crow,     Franklin). -   [Funayama, 2012] U.S. Pat. No. 8,165,401 (Funayama, et al.). -   [Harris C., 1988] “A combined corner and edge detector” (Harris C.,     Stephens M.). -   [Herbert Bay, 2008] “Surf: Speeded up robust features” (Herbert Bay,     Andreas Ess, Tinne Tuytelaars, Luc Van Gool). -   [Lin, 2010] U.S. Pat. No. 7,668,376 (Lin, et al.). -   [Lowe, 2000] U.S. Pat. No. 6,711,293 (Lowe). -   [Lowe, 2004] “Distinctive image features from scale-invariant     keypoints” (Lowe, D. G.). -   [Rosten and Drummond, 2006] “Machine learning for high-speed corner     detection” (E. Rosten and T. Drummond). -   [Takaki, 2011] U.S. Pat. No. 8,027,514 (Takaki, et al.). -   [Troyanker, 2003] U.S. Pat. No. 6,563,959 (Troyanker). -   [Wang, 2012] U.S. Pat. No. 8,233,711 (Wang, et al.). 

What is claimed is:
 1. A method for deriving descriptor of an image area having a plurality of pixels, the interest point having a location in the image and a scale, the method comprising: identifying a neighborhood around the interest point location proportional in size to the interest point's scale, the neighborhood being divided into subregions, each subregion comprising a set of pixels; creating a frequency domain representation of each subregion; normalizing frequency domain coefficients; selecting the normalized frequency domain coefficients to comprise a multidimensional descriptor.
 2. The method of claim 1, wherein the frequency domain representation of the interest point subregion is calculated using integral images;
 3. The method of claim 1, further comprising weighting the frequency domain coefficients before or after normalization;
 4. The method of claim 3, wherein coefficient weights are derived from a training set of descriptors from the same of from different images;
 5. The method of claim 4, wherein inverse medians of corresponding descriptor components from a training set are used as weighting coefficients;
 6. The method of claim 1, wherein frequency domain representation of the subregion is calculated as a 2-dimensional DCT of the subarea luminosity;
 7. The method of claim 1, further comprising encoding the derived multidimensional descriptor to a bitwise representation;
 8. The method of claim 7, wherein Hamming distance between descriptors is used as the distance measure;
 9. The method of claim 7, wherein the bitwise descriptor representation is formed by results of comparisons of descriptor components to threshold values;
 10. The method of claim 9, wherein the threshold values are calculated from a training set of descriptors;
 11. A computer based system implementing the method of claim
 1. 